Cognitive Structural Equation Models

认知结构方程模型

• 批准号: 1230118
• 负责人: Joachim Vandekerckhove
• 金额: $ 25万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1230118&HistoricalAwards=false
• 关键词: Cognitive; Structural; Equation; Models

This project will combine two types of data analysis strategies that are common in different fields. In cognitive psychology, the state of the art is cognitive process modeling. Data are analyzed by fitting mathematical representations of cognitive functions to data and interpreting the obtained parameter estimates. In psychometrics, the most common form of data analysis involves latent variable modeling. Batteries of small tests, each individual test imperfect, are jointly analyzed to uncover unobservable underlying factors, such as general intelligence or specific abilities. Cognitive modeling succeeds in extracting more information from data, whereas psychometrical methods are useful for pooling information across tasks or participants. Combining these two traditions involves the formal challenges of applying latent variable structure to cognitive model parameters, integrating the mathematical assumptions of both strategies, and investigating the effects of those combined assumptions. The project also involves technical challenges, such as implementing the methods in software. A new hybrid method called cognitive structural equation modeling will be applied in a retrospective analyses of data on cognitive executive functions and data on facets of working memory. Additionally, a cognitive structural equation model will be used to investigate the stability of participants' behavior in cognitive tasks over time.The new method will be particularly well suited for the simultaneous analysis of different cognitive tasks in order to uncover underlying structure in participants' aptitude in the tasks. Improvements in psychological measurement are potentially useful in a variety of contexts, ranging from fundamental research in perception, cognition, memory, decision making, emotion, and development, to applied measurement in educational testing, job selection, and psychodiagnosis. Software developed as part of this project will be made freely available to researchers.

该项目将结合不同领域常见的两种数据分析策略。 在认知心理学中，最先进的是认知过程建模。 通过将认知功能的数学表示与数据拟合并解释获得的参数估计来分析数据。 在心理测量学中，最常见的数据分析形式涉及潜变量建模。 一系列小型测试（每个测试都不完美）被联合分析，以揭示不可观察的潜在因素，例如一般智力或特定能力。 认知建模成功地从数据中提取更多信息，而心理测量方法可用于跨任务或参与者汇集信息。 将这两种传统结合起来涉及将潜在变量结构应用于认知模型参数、整合两种策略的数学假设以及研究这些组合假设的影响的正式挑战。 该项目还涉及技术挑战，例如在软件中实施这些方法。 一种称为认知结构方程模型的新混合方法将应用于认知执行功能数据和工作记忆方面数据的回顾性分析。 此外，认知结构方程模型将用于研究参与者在认知任务中的行为随时间的稳定性。新方法将特别适合同时分析不同的认知任务，以揭示参与者在任务中的能力的潜在结构。 心理测量的改进在各种背景下都可能有用，从感知、认知、记忆、决策、情感和发展的基础研究，到教育测试、工作选择和心理诊断的应用测量。 作为该项目一部分开发的软件将免费提供给研究人员。